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Directed testing

Technique WIKI v1 · 6/25/2026

Directed testing is a hardware and digital-system verification technique in which verification engineers hand-craft specific test cases to exercise particular functionalities or corner cases of a design, rather than relying on randomly generated stimuli. It is one of the traditional 'workhorse' methodologies of digital design verification, valued for being precise and targeted but limited by the substantial manual effort required to anticipate issues and by scaling challenges as design complexity grows. In modern multi-layer verification flows it is typically combined with constrained-random testing and benchmark-driven validation, and is supported by compliance suites such as riscv-tests and riscv-arch-tests for RISC-V cores.

Overview

Directed testing is a verification methodology in which engineers deliberately construct specific test cases to validate targeted functionalities, corner cases, or microarchitectural scenarios of a design under test (DUT). It is described as one of the traditional 'workhorses' of digital-system verification, alongside constrained-random testing, and is characterized as precise and targeted but requiring significant manual effort to author comprehensive test suites [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].

Definition and characteristics

In the context of digital hardware verification, directed testing involves:

  • Creating specific test cases to verify particular functionalities or corner cases of a design.
  • Requiring verification engineers to anticipate potential issues and create tests to expose them.
  • Being precise and targeted, but increasingly difficult to scale as system complexity grows.

The approach becomes challenging as design complexity grows, because engineers must anticipate all the corner cases and interactions worth probing [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].

Role in multi-layer verification flows

Directed testing is commonly embedded as the first phase of a layered verification strategy. In the NaxRiscv SoC verification flow, for instance, the methodology is described as a multi-layered approach that 'combines directed testing, real-world benchmarking, and constrained-random testing' to ensure comprehensive evaluation of both architectural and microarchitectural aspects of the design [chunk:e8c8a109-6fbe-4965-932d-6dd8a9020ec5]. The strategy begins with an automated Jenkins Continuous Integration (CI) pipeline that executes standard compliance test suites — specifically riscv-tests and riscv-arch-tests — as established benchmarks for verifying that the Instruction Set Architecture (ISA) is correctly implemented, including extensions (F, D, C) and proper interactions with system registers (CSRs). Following these directed tests, the methodology incorporates real-world benchmarking (CoreMark, Dhrystone, OS boot sequences) and constrained-random testing using RISCV-DV [chunk:e8c8a109-6fbe-4965-932d-6dd8a9020ec5].

Comparison with constrained-random testing

Directed testing is positioned in contrast with constrained-random testing:

  • Directed testing creates specific test cases to verify particular functionalities or corner cases; it is precise and targeted but requires substantial manual effort [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].
  • Constrained-random testing generates a large number of random test cases within specified constraints that limit the search to interesting scenarios, but may still miss rare corner cases and can be inefficient in the number of tests required to achieve adequate coverage [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].

Related and evolving techniques

Several adjacent or derivative approaches are reported in the literature:

  • Module-directed testing (e.g., MoDitector for autonomous driving systems) extends the directed idea to module-level fault attribution: it employs module-specific oracles, module-specific feedback, adaptive seed selection, and mutation to generate tests that provoke failures in specific subsystems rather than only detecting collisions in a black-box manner [source:arxiv:2502.08504v1].
  • Coverage-directed test selection uses supervised learning on coverage feedback to bias selection of tests toward those likely to increase functional coverage, reducing manual constraint writing and accelerating coverage closure in simulation-based verification [source:arxiv:2205.08524v3].
  • Reinforcement-learning-driven test generation has been explored for RISC-V functional verification, where an RL agent guides instruction generation based on coverage metrics and CPU state, with reported coverage improvements over random instruction generation [chunk:d4d5f66e-2a3c-4123-80b0-2be363bd78fe].

RISC-V context

For RISC-V cores specifically, directed testing typically relies on established open compliance suites. The riscv-tests suite provides ISA-level compliance tests, while riscv-arch-tests provides architectural compliance tests; together they form the foundation of the directed-testing phase in many RISC-V verification flows [chunk:e8c8a109-6fbe-4965-932d-6dd8a9020ec5].

Limitations

  • Requires significant manual effort to author comprehensive test suites [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].
  • Engineers must anticipate potential issues ahead of time, which becomes increasingly difficult as system complexity grows [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].
  • In RISC-V, the modular nature of the architecture — with many optional extensions and custom instructions — multiplies the number of configurations that need to be verified, making purely manual directed-testing approaches harder to scale [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].

LINKED ENTITIES

2 links
riscv-tests IMPLEMENTS
riscv-arch-tests IMPLEMENTS

CITATIONS

7 sources
7 citations
[1] Directed testing involves creating specific test cases to verify particular functionalities or corner cases of a design; it is precise and targeted but requires significant manual effort. Reinforcement Learning Framework for RISC-V Functional Verification
[2] The NaxRiscv verification methodology is structured as a multi-layered approach that combines directed testing, real-world benchmarking, and constrained-random testing, beginning with execution of the riscv-tests and riscv-arch-tests compliance suites in a Jenkins CI pipeline. Comprehensive lockstep verification for NaxRiscv SoC integrating a superscalar core supporting multiple ISA extensions
[3] Directed and constrained-random testing are described as the traditional 'workhorses' of digital system verification, with constrained-random testing potentially missing rare corner cases and being inefficient in test count, motivating machine-learning-based alternatives. Reinforcement Learning Framework for RISC-V Functional Verification
[4] RISC-V's modular approach with many optional extensions and custom instructions multiplies the number of configurations that must be verified, making purely manual directed-testing approaches harder to scale. Reinforcement Learning Framework for RISC-V Functional Verification
[5] Module-directed testing (e.g., MoDitector) extends directed-testing ideas to module-level fault attribution in autonomous driving systems, using module-specific oracles, feedback, adaptive seed selection, and mutation to provoke module-specific failures. MoDitector: Module-Directed Testing for Autonomous Driving Systems
[6] Coverage-directed test selection uses supervised learning from coverage feedback to bias test selection toward tests likely to increase functional coverage, reducing manual constraint writing and accelerating coverage closure in simulation-based verification. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification
[7] An RL-driven framework for RISC-V functional verification uses coverage metrics and CPU state (register file, program counter) to guide automatic assembly-code generation, reporting a coverage improvement over random instruction generation. Reinforcement Learning Framework for RISC-V Functional Verification